Approximations for Binary Gaussian Process Classification

2008

Article

ei

We provide a comprehensive overview of many recent algorithms for approximate inference in
Gaussian process models for probabilistic binary classification. The relationships between several
approaches are elucidated theoretically, and the properties of the different algorithms are
corroborated by experimental results. We examine both 1) the quality of the predictive distributions and
2) the suitability of the different marginal likelihood approximations for model selection (selecting
hyperparameters) and compare to a gold standard based on MCMC. Interestingly, some methods
produce good predictive distributions although their marginal likelihood approximations are poor.
Strong conclusions are drawn about the methods: The Expectation Propagation algorithm is almost
always the method of choice unless the computational budget is very tight. We also extend
existing methods in various ways, and provide unifying code implementing all approaches.

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